Predictive energy management with engine switching control for hybrid electric vehicle via ADMM

This paper studies energy management (EM) of a power-split hybrid electric vehicle (HEV) equipped with planetary gear sets. We first formulate a mixed-integer global optimal control problem that includes a binary switching variable. Convex modeling, including the fuel model for a compound energy conversion unit, is then presented to reformulate the mixed-integer EM as a two-step program. For optimizing the engine switching and battery power decisions in the first step, we employ the alternating direction method of multipliers (ADMM) algorithm where the solution of the convex relaxation is used to initialize the non-convex problem. On the standard driving cycle, simulation results indicate that the ADMM based EM method saves 7.63% fuel compared to a heuristic method, and shows 99% optimality compared to a dynamic programming method, while saving three orders of magnitude in computing time. An ADMM combined model predictive control (ADMM-MPC) method is also developed that is suitable for receding horizon control scenarios. The ADMM-MPC method shows 5.28% fuel saving when implemented using a prediction horizon of 15 samples. Meanwhile, the mean computing time for MPC updates is 3.53 ms. Our results demonstrate that the proposed ADMM is capable of real-time control.

Automated summary

This paper addresses the energy management problem of a power-split hybrid electric vehicle (HEV) with planetary gear sets. A mixed-integer global optimal control problem is formulated, and convex modeling is presented to reformulate the problem as a two-step program. The alternating direction method of multipliers (ADMM) algorithm is employed to optimize the engine switching and battery power decisions. Simulation results show significant fuel savings and computational efficiency compared to heuristic and dynamic programming methods. An ADMM-MPC method is also developed for real-time control with promising results.